 I'm going to give you a really brief tour of ENCODE. I'd like to start off by letting you know our objectives for this. We'd like to tell you something about ENCODE and roadmap epigenomics so that you can learn about how to use it in your work. We'd also like to hear from everybody what are ways we can increase different projects more useful, either at the interface level or in the way that we're actually doing the stuff. I'd also like to thank my colleagues in ENCODE. You can see an ENCODE consortium photo on the bottom and also two of my program colleagues, Elise Finegold has been within ENCODE since its inception. So ENCODE is the encyclopedia of DNA elements. And the goal of the project is to identify all of the functional elements in the genome. This is, of course, an aspirational goal. There's no way to actually know whether this is complete or how complete it is. And we're also trying to want to make this resource freely available to the community. This is a major portion, a major idea behind the project. And we're hoping that people can use it in studies of disease and studies of gene regulation. And ENCODE has built on decades of research in gene regulation. What we've done is to take assays that have been used for many, many years to study gene regulation and look at the mechanism of gene regulation and either use other people's versions of genome-wide assays or developed our own to apply these assays to look at the whole genome. Since these assays are mechanistically linked to gene regulation, we and other projects are using them to reverse engineer the function of the genome. We're trying to determine where there are genes, the transcripts that come from those genes, and also the regulatory elements that control the action of those genes. So one important goal for NHGRI, an important goal for ENCODE, is to understand the whole genome. Today, I'm going to focus on the non-coding portions of the genome because we think it's relatively understudied. And bulk studies of the genome, for instance, GWAS studies, find that most of the associations are in non-coding regions of the genome. Also, if you look at individual gene studies, you can find genes like Fragile X and Mandelian Disorder where the known variant is a non-coding variant that accounts for almost all of the heritability and even polygenic diseases like ALS, where the variant that accounts for the largest known chunk of heritability is a non-coding variant. So the idea of how ENCODE might help people is if we look for variants, the way we understand them is to put them in context. We associate them with things like maps or drawings. And ENCODE is trying to make those maps richer. So here's a cartoon on the slide showing a variant that's been associated with allergy and asthma. And you can see the arrow pointing to the SNP and showing where it is in its genome context. And that's a great start. But what we can do with ENCODE is we can add different biochemical assays from genome-wide. And from this we can see that it appears that this variant lies within what could be a regulatory region. So that's one additional piece of information that you get. It turns out that this regulatory region is especially prominent in a particular cell type. So that's another piece of information that you could get from ENCODE. This regulatory, this candidate regulatory element lies within a gene. But what you can learn from ENCODE is you can make a prediction of what gene it might work with. And what's predicted is that it works with these neighboring genes. And I'm showing you this example because this locus has been extensively studied in the mouse where it's well known that in fact this is a locus control region that regulates those neighboring genes. But you could make this prediction from ENCODE human data. That's how that works. So you probably heard me say a lot about prediction and ENCODE can tell you that you might be able to figure out things. Primary use for ENCODE is hypothesis generation, I would say. And people are using ENCODE to go from associated variants to what might be causal variants. They're using ENCODE to say from associated variants might be what might be the target cell type that the variant works in because many diseases affect multiple cell types. You might ask from a target variant, what are the regulatory regions? What are the upstream regulatory factors that are working to control that gene? And you might also be using ENCODE to make predictions about how that candidate regulatory element is working. And at the bottom of the slide, the focus of what a lot of you'll hear today is about using germline genetic studies. But I would point out that if you have an epigenomic cohort, ENCODE should work well with that. If you work on epigenomics, then there are additional caveats that one would need to apply. And also this works quite well with somatic variants. Again, there are additional caveats that one would have to apply. So we think ENCODE is quite useful. We see it being used in a lot of different ways. And we maintain on our portal a list of publications that have used ENCODE data. And ENCODE has been in a number of talks from the sessions at this ASHD meeting as well. It's been in over a thousand papers from the community. That means people that are using ENCODE data but don't have ENCODE funding. And a large fraction of these, about one third of them are in human disease. So I think that attests to the translational value of the resource. So ENCODE has released thousands of data sets and they're shared freely. They're available through the ENCODE portal, ENCODEproject.org. There's no login or controlled access required. They're available to everybody. We're also releasing software that we've developed and sharing software that our colleagues outside of ENCODE have developed but we've found to be useful. We're working really hard to increase data interoperability. We're working for instance with roadmap epigenomics and other IHEC projects to try and standardize our interfaces and data. And I'd like to say a little bit about IHEC, the International Human Epigenome Consortium. And CODE is one of the members of IHEC. You'll also hear from roadmap next which is the IHEC member. And IHEC has its own data portal. Again, these slides are shared so you don't need to write down the URLs now but I'd encourage you to go see their portal and see the data from other projects such as the Canadian projects, Blueprint and Deep and so forth. And lastly, again, I don't expect anybody to be writing this down or sharing the slides but there are a lot of different ways to access ENCODE. There are a number of useful URLs here. Again, the portal is ENCODEproject.org. We have tutorials on ENCODE portal. We also have tutorials available at the NHGRI website, genome.gov. There are links to get to IHEC resources as well. And you can also join the ENCODE mailing list if you wish to hear the latest updates.